What do you mean by sampling? Explain the difference between stratified sampling and cluster sampling.

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When one by one study of all units of a population is not possible due to some factors like time, cost, manpower, resources and destructive nature of study, we take a small representative part from the population for the study. This small represenatative part selected for the study from the population is called sample.

The process of selecteding a sample from a population is called sampling.

For Example: A pathologist takes a syringe of blood as a sample to find out a disease.

Difference between strtified and cluster sampling:

BASIS STRATIFIED SAMPLING CLUSTER SAMPLING
Meaning Stratified sampling is one, in which the population is divided into homogeneous segments, and then the sample is randomly taken from the segments. Cluster sampling refers to a sampling method wherein the members of the population are selected at random, from naturally occurring groups called ‘cluster’.
Sample Randomly selected individuals are taken from all the strata. All the individuals are taken from randomly selected clusters.
Selection of population elements Individually Collectively
Homogeneity Within group Between groups
Heterogeneity Between groups Within group
Bifurcation Imposed by the researcher Naturally occurring groups
Objective To increase precision and representation. To reduce cost and improve efficiency.
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